2 citations · 9 across the 19 of their papers we have counts for
6 papers · 1 filter
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model. This approach is promising because it, in contras…
SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
Takanori Ashihara, Takafumi Moriya, Kohei Matsuura +5
Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition. More recently, speech SS…
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai +7
Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for…
Improving Scheduled Sampling for Neural Transducer-based ASR
Takafumi Moriya, Takanori Ashihara, Hiroshi Sato +3
The recurrent neural network-transducer (RNNT) is a promising approach for automatic speech recognition (ASR) with the introduction of a prediction network that autoregressively co…
Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models
Takanori Ashihara, Takafumi Moriya, Kohei Matsuura +1
Self-supervised learning (SSL) has been dramatically successful not only in monolingual but also in cross-lingual settings. However, since the two settings have been studied indivi…
Leveraging Large Text Corpora for End-to-End Speech Summarization
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech re…